The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research Article Swipe
Jasin Machkour
,
Michael Muma
,
Daniel P. Palomar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.05211
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.05211
Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of associations improve our understanding of complex polygenic diseases and enable the development of new drugs and personalized medicine. Thus, fast multivariate variable selection methods that have a high true positive rate (TPR) while controlling the false discovery rate (FDR) are crucial. Recently, the T-Rex+GVS selector, a version of the T-Rex selector that uses the elastic net (EN) as a base selector to perform grouped variable election, was proposed. Although it significantly increased the TPR in simulated GWAS compared to the original T-Rex, its comparably high computational cost limits scalability. Therefore, we propose the informed elastic net (IEN), a new base selector that significantly reduces computation time while retaining the grouped variable selection property. We quantify its grouping effect and derive its formulation as a Lasso-type optimization problem, which is solved efficiently within the T-Rex framework by the terminated LARS algorithm. Numerical simulations and a GWAS study demonstrate that the proposed T-Rex+GVS (IEN) exhibits the desired grouping effect, reduces computation time, and achieves the same TPR as T-Rex+GVS (EN) but with lower FDR, which makes it a promising method for large-scale GWAS.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.05211
- https://arxiv.org/pdf/2410.05211
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403324147
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https://openalex.org/W4403324147Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.05211Digital Object Identifier
- Title
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The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics ResearchWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-07Full publication date if available
- Authors
-
Jasin Machkour, Michael Muma, Daniel P. PalomarList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.05211Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.05211Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.05211Direct OA link when available
- Concepts
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Genomics, Selection (genetic algorithm), Elastic net regularization, Control (management), Computational biology, Variable (mathematics), Biology, Feature selection, Computer science, Genetics, Mathematics, Genome, Artificial intelligence, Gene, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.simulations | 172 |
| abstract_inverted_index.associations | 31 |
| abstract_inverted_index.multivariate | 51 |
| abstract_inverted_index.optimization | 156 |
| abstract_inverted_index.personalized | 47 |
| abstract_inverted_index.reproducible | 26 |
| abstract_inverted_index.scalability. | 119 |
| abstract_inverted_index.computational | 116 |
| abstract_inverted_index.significantly | 101, 133 |
| abstract_inverted_index.understanding | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |